install.packages("filelock")
q()
n
+df
+df
+outcome_formula <- y ~ x + z
+outcome_family=gaussian()
+proxy_formula <- w_pred ~ x
+truth_formula <- x ~ z
+params <- start
+ll.y.obs.x0
+ll.y.obs.x1
+rater_formula <- x.obs ~ x
+rater_formula
+rater.modle.matrix.obs.x0
+rater.model.matrix.obs.x0
+names(rater.model.matrix.obs.x0)
+head(rater.model.matrix.obs.x0)
+df.obs
+ll.x.obs.0
+rater.params
+rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$xobs.0==1])
+df.obs$xobs.0==1
+df.obs$x.obs.0==1
+ll.x.obs.0[df.obs$x.obs.0==1]
+rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
+df.obs$x.obs.0==1
+n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2]
+ rater.params <- params[param.idx:n.rater.model.covars]
+rater.params
+ ll.x.obs.0[df.obs$x.obs.0==1] <- plogis(rater.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE)
+t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]
+)
+dimt(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,])
+dim(t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]))
+dim(ll.x.obs.0[df.obs$x.obs.0==1])
+rater.params
+rater.params
+rater.params
+rater_formula
+rater.params
+)
+1+1
+q()
+n
+outcome_formula <- y ~ x + z
+proxy_formula <- w_pred ~ x + z + y
+truth_formula <- x ~ z
+proxy_formula
+eyboardio Model 01 - Kaleidoscope locally built
+df <- df.triple.proxy.mle
+outcome_family='gaussian'
+outcome_family=gaussian()
+proxy_formulas=list(proxy_formula,x.obs.0~x, x.obs.1~x)
+proxy_formulas
+proxy_familites <- rep(binomial(link='logit'),3)
+proxy_families = rep(binomial(link='logit'),3)
+proxy_families
+proxy_families = list(binomial(link='logit'),binomial(link='logit'),binomial(link='logit'))
+proxy_families
+proxy_families[[1]]
+proxy.params
+i
+proxy_params
+proxy.params
+params
+params <- start
+df.triple.proxy.mle
+df
+coder.formulas <- c(x.obs.0 ~ x, x.obs.1 ~x)
+outcome.formula
+outcome_formula
+depvar(outcome_formula
+)
+outcome_formula$terms
+terms(outcome_formula)
+q()
+n
+df.triple.proxy.mle
+triple.proxy.mle
+df
+df <- df.triple.proxy
+outcome_family <- binomial(link='logit')
+outcome_formula <- y ~x+z
+proxy_formula <- w_pred ~ y
+coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit'))
+coder_formulas=list(y.obs.1~y,y.obs.2~y); proxy_formula=w_pred~y; proxy_family=binomial(link='logit')
+coder_formulas=list(y.obs.0~y,y.obs.1~y)
+traceback()
+df
+df
+outcome.model.matrix
+q()
+n
simulate_data <- function(N, m, B0=0, Bxy=0.2, Bzy=-0.2, Bzx=0.2, y_explained_variance=0.025, prediction_accuracy=0.73, seed=1){
set.seed(seed)
- z <- rbinom(N, 1, 0.5)
+ z <- rnorm(N,sd=0.5)
# x.var.epsilon <- var(Bzx *z) * ((1-zx_explained_variance)/zx_explained_variance)
xprime <- Bzx * z #+ x.var.epsilon
x <- rbinom(N,1,plogis(xprime))
parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3)
parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
-parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3)
+parser <- add_argument(parser, "--Bxy", help='Effect of x on y', default=0.3)
args <- parse_args(parser)
B0 <- 0
Bzy <- args$Bzy
Bzx <- args$Bzx
-if (args$m < args$N){
+df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy)
- df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy)
+result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='')
- result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='')
-
- outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula))
+outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula))
- outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
- if(file.exists(args$outfile)){
- logdata <- read_feather(args$outfile)
- logdata <- rbind(logdata,as.data.table(outline),fill=TRUE)
- } else {
- logdata <- as.data.table(outline)
- }
-
- print(outline)
- write_feather(logdata, args$outfile)
- unlock(outfile_lock)
+outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
+if(file.exists(args$outfile)){
+ logdata <- read_feather(args$outfile)
+ logdata <- rbind(logdata,as.data.table(outline),fill=TRUE)
+} else {
+ logdata <- as.data.table(outline)
}
+
+print(outline)
+write_feather(logdata, args$outfile)
+unlock(outfile_lock)
+
## one way to do it is by adding correlation to x.obs and y that isn't in w.
## in other words, the model is missing an important feature of x.obs that's related to y.
-simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,accuracy_imbalance_difference=0.3){
+simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8,z_bias=0,accuracy_imbalance_difference=0.3){
set.seed(seed)
# make w and y dependent
- z <- rbinom(N, 1, plogis(qlogis(0.5)))
- x <- rbinom(N, 1, plogis(Bzx * z + qlogis(0.5)))
+ z <- rnorm(N,sd=0.5)
+ x <- rbinom(N, 1, plogis(Bzx * z))
y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bzy*z,Bxy*x)) * ((1-y_explained_variance)/y_explained_variance)
y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon))
## print(mean(df$w_pred == df$x))
resids <- resid(lm(y~x + z))
- odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1]))
- odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0]))
+ odds.x1 <- qlogis(prediction_accuracy) + y_bias*qlogis(pnorm(resids[x==1])) + z_bias * qlogis(pnorm(z,sd(z)))
+ odds.x0 <- qlogis(prediction_accuracy,lower.tail=F) + y_bias*qlogis(pnorm(resids[x==0])) + z_bias * qlogis(pnorm(z,sd(z)))
## acc.x0 <- p.correct[df[,x==0]]
## acc.x1 <- p.correct[df[,x==1]]
parser <- add_argument(parser, "--seed", default=51, help='seed for the rng')
parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1)
-parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.8)
+parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.75)
parser <- add_argument(parser, "--accuracy_imbalance_difference", help='how much more accurate is the predictive model for one class than the other?', default=0.3)
parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3)
parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3)
parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y*z*x")
-parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-1)
+parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.5)
+parser <- add_argument(parser, "--z_bias", help='coefficient of z on the probability a classification is correct', default=0)
parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
args <- parse_args(parser)
## one way to do it is by adding correlation to x.obs and y that isn't in w.
## in other words, the model is missing an important feature of x.obs that's related to y.
-simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, log.likelihood.gain = 0.1){
+simulate_data <- function(N, m, B0, Bxy, Bzy, Bzx, seed, prediction_accuracy=0.73, log.likelihood.gain = 0.1){
set.seed(seed)
set.seed(seed)
# make w and y dependent
- z <- rbinom(N, 1, 0.5)
- x <- rbinom(N, 1, 0.5)
+ z <- rnorm(N, sd=0.5)
+ x <- rbinom(N, 1, plogis(Bzx*z))
ystar <- Bzy * z + Bxy * x + B0
y <- rbinom(N,1,plogis(ystar))
## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.01)
parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.01)
+parser <- add_argument(parser, "--Bzx", help='coeffficient of z on x', default=-0.5)
parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")
B0 <- 0
Bxy <- args$Bxy
Bzy <- args$Bzy
-
+Bzx <- args$Bzx
if(args$m < args$N){
- df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy)
+ df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, args$seed, args$prediction_accuracy)
# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
- result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+ result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'Bzx'=Bzx,'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
## one way to do it is by adding correlation to x.obs and y that isn't in w.
## in other words, the model is missing an important feature of x.obs that's related to y.
-simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, x_bias=-0.75){
+simulate_data <- function(N, m, B0, Bxy, Bzy, seed, prediction_accuracy=0.73, z_bias=-0.75){
set.seed(seed)
# make w and y dependent
- z <- rbinom(N, 1, 0.5)
- x <- rbinom(N, 1, 0.5)
+ z <- rnorm(N,sd=0.5)
+ x <- rbinom(N,1,0.5)
ystar <- Bzy * z + Bxy * x + B0
y <- rbinom(N,1,plogis(ystar))
df <- df[, y.obs := y]
}
- odds.y1 <- qlogis(prediction_accuracy) + x_bias*df[y==1]$x
- odds.y0 <- qlogis(prediction_accuracy,lower.tail=F) + x_bias*df[y==0]$x
+ odds.y1 <- qlogis(prediction_accuracy) + z_bias*df[y==1]$z
+ odds.y0 <- qlogis(prediction_accuracy,lower.tail=F) + z_bias*df[y==0]$z
df[y==0,w:=plogis(rlogis(.N,odds.y0))]
df[y==1,w:=plogis(rlogis(.N,odds.y1))]
parser <- add_argument(parser, "--N", default=1000, help="number of observations of w")
parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
parser <- add_argument(parser, "--seed", default=17, help='seed for the rng')
-parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
-parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.005)
-parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.8)
-## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75)
-## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
-parser <- add_argument(parser, "--x_bias", help='how is the classifier biased?', default=0.75)
-parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3)
-parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3)
+parser <- add_argument(parser, "--outfile", help='output file', default='example_4.feather')
+parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.79)
+## parser <- add_argument(parser, "--z_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75)
+## parser <- add_argument(parser, "--z_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
+parser <- add_argument(parser, "--z_bias", help='how is the classifier biased?', default=1.5)
+parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.1)
+parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.1)
parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
-parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y+x")
+parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y+z")
args <- parse_args(parser)
if(args$m < args$N){
- df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$x_bias)
+ df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$z_bias)
-# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
- result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias'=args$x_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias_y0'=args$z_bias_y0,'z_bias_y1'=args$z_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+ result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'z_bias'=args$z_bias,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
y.var.epsilon <- (var(Bzy * z) + var(Bxy *x) + 2*cov(Bxy*x,Bzy*z)) * ((1-y_explained_variance)/y_explained_variance)
y.epsilon <- rnorm(N, sd = sqrt(y.var.epsilon))
- y <- Bzy * z + Bxy * x + y.epsilon
+ y <- Bzy * z + Bxy * x + y.epsilon + B0
df <- data.table(x=x,y=y,z=z)
df <- df[, x.obs := x]
}
- df[ (!is.na(x.obs)) ,x.obs.0 := abs(x.obs - rbinom(.N, 1, 1-coder_accuracy))]
- df[ (!is.na(x.obs)) ,x.obs.1 := abs(x.obs - rbinom(.N, 1, 1-coder_accuracy))]
-
+ coder.0.correct <- rbinom(m, 1, coder_accuracy)
+ coder.1.correct <- rbinom(m, 1, coder_accuracy)
+
+ df[!is.na(x.obs),x.obs.0 := as.numeric((x.obs & coder.0.correct) | (!x.obs & !coder.0.correct))]
+ df[!is.na(x.obs),x.obs.1 := as.numeric((x.obs & coder.1.correct) | (!x.obs & !coder.1.correct))]
+
## how can you make a model with a specific accuracy?
w0 =(1-x)**2 + (-1)**(1-x) * prediction_accuracy
parser <- arg_parser("Simulate data and fit corrected models")
parser <- add_argument(parser, "--N", default=1000, help="number of observations of w")
-parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
-parser <- add_argument(parser, "--seed", default=57, help='seed for the rng')
+parser <- add_argument(parser, "--m", default=150, help="m the number of ground truth observations")
+parser <- add_argument(parser, "--seed", default=1, help='seed for the rng')
parser <- add_argument(parser, "--outfile", help='output file', default='example_1.feather')
-parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.05)
+parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1)
# parser <- add_argument(parser, "--zx_explained_variance", help='what proportion of the variance of x can be explained by z?', default=0.3)
parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.73)
-parser <- add_argument(parser, "--coder_accuracy", help='how accurate is the predictive model?', default=0.8)
+parser <- add_argument(parser, "--coder_accuracy", help='how accurate are the human coders?', default=0.85)
parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~x")
# parser <- add_argument(parser, "--rater_formula", help='formula for the true variable', default="x.obs~x")
parser <- add_argument(parser, "--truth_formula", help='formula for the true variable', default="x~z")
parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=-0.3)
-parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
-parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3)
+parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=0.27)
+parser <- add_argument(parser, "--Bxy", help='Effect of x on y', default=-0.33)
args <- parse_args(parser)
B0 <- 0
if (args$m < args$N){
- df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed + 500, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy, coder_accuracy=args$coder_accuracy)
+ df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, Bzx, seed=args$seed, y_explained_variance = args$y_explained_variance, prediction_accuracy=args$prediction_accuracy, coder_accuracy=args$coder_accuracy)
result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'outcome_formula'=args$outcome_formula, 'truth_formula'=args$truth_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, 'coder_accuracy'=args$coder_accuracy, error='')
df <- data.table(x=x,y=y,ystar=ystar,z=z)
- if(m < N){
- df <- df[sample(nrow(df), m), y.obs := y]
- } else {
- df <- df[, y.obs := y]
- }
+ df <- df[sample(nrow(df), m), y.obs := y]
- df[ (!is.na(y.obs)) ,y.obs.0 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))]
- df[ (!is.na(y.obs)) ,y.obs.1 := abs(y.obs - rbinom(.N, 1, 1-coder_accuracy))]
+ coder.0.correct <- rbinom(m, 1, coder_accuracy)
+ coder.1.correct <- rbinom(m, 1, coder_accuracy)
+
+ df[!is.na(y.obs),y.obs.0 := as.numeric((.SD$y.obs & coder.0.correct) | (!.SD$y.obs & !coder.0.correct))]
+ df[!is.na(y.obs),y.obs.1 := as.numeric((.SD$y.obs & coder.1.correct) | (!.SD$y.obs & !coder.1.correct))]
odds.y1 <- qlogis(prediction_accuracy)
odds.y0 <- qlogis(prediction_accuracy,lower.tail=F)
df[,w_pred := as.integer(w > 0.5)]
+ print(mean(df$y == df$y.obs.0,na.rm=T))
+ print(mean(df$y == df$y.obs.1,na.rm=T))
+
print(mean(df[x==0]$y == df[x==0]$w_pred))
print(mean(df[x==1]$y == df[x==1]$w_pred))
print(mean(df$w_pred == df$y))
}
parser <- arg_parser("Simulate data and fit corrected models")
-parser <- add_argument(parser, "--N", default=1000, help="number of observations of w")
+parser <- add_argument(parser, "--N", default=5000, help="number of observations of w")
parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
-parser <- add_argument(parser, "--seed", default=17, help='seed for the rng')
+parser <- add_argument(parser, "--seed", default=16, help='seed for the rng')
parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
-parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.005)
-parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.72)
+parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.1)
+parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.73)
## parser <- add_argument(parser, "--x_bias_y1", help='how is the classifier biased when y = 1?', default=-0.75)
## parser <- add_argument(parser, "--x_bias_y0", help='how is the classifier biased when y = 0 ?', default=0.75)
parser <- add_argument(parser, "--Bxy", help='coefficient of x on y', default=0.3)
parser <- add_argument(parser, "--Bzy", help='coeffficient of z on y', default=-0.3)
parser <- add_argument(parser, "--outcome_formula", help='formula for the outcome variable', default="y~x+z")
-parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y")
+parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~y+y.obs.1+y.obs.0")
parser <- add_argument(parser, "--coder_accuracy", help='How accurate are the coders?', default=0.8)
args <- parse_args(parser)
Bzy <- args$Bzy
-if(args$m < args$N){
- df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$coder_accuracy)
-
-# result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
- result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+df <- simulate_data(args$N, args$m, B0, Bxy, Bzy, args$seed, args$prediction_accuracy, args$coder_accuracy)
- outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
+ # result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'x_bias_y0'=args$x_bias_y0,'x_bias_y1'=args$x_bias_y1,'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
+result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'outcome_formula' = args$outcome_formula, 'proxy_formula' = args$proxy_formula)
- outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
+outline <- run_simulation_depvar(df, result, outcome_formula = as.formula(args$outcome_formula), proxy_formula = as.formula(args$proxy_formula))
- if(file.exists(args$outfile)){
- logdata <- read_feather(args$outfile)
- logdata <- rbind(logdata,as.data.table(outline),fill=TRUE)
- } else {
- logdata <- as.data.table(outline)
- }
+outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
- print(outline)
- write_feather(logdata, args$outfile)
- unlock(outfile_lock)
+if(file.exists(args$outfile)){
+ logdata <- read_feather(args$outfile)
+ logdata <- rbind(logdata,as.data.table(outline),fill=TRUE)
+} else {
+ logdata <- as.data.table(outline)
}
+
+print(outline)
+write_feather(logdata, args$outfile)
+unlock(outfile_lock)
+
+warnings()
SHELL=bash
-Ns=[1000, 2000, 4000]
-ms=[100, 200, 400, 800]
-seeds=[$(shell seq -s, 1 250)]
+Ns=[1000, 5000, 10000]
+ms=[100, 200, 400]
+seeds=[$(shell seq -s, 1 500)]
explained_variances=[0.1]
all:remembr.RDS remember_irr.RDS
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 test_true_z_jobs
-example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py
- sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[0.3]}' --outfile example_1_jobs
+example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py pl_methods.R
+ sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs
example_1.feather: example_1_jobs
rm -f example_1.feather
- sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
-# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_1_jobs
+ sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_1_jobs
+ sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_1_jobs
+ sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_1_jobs
+ sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_1_jobs
+ sbatch --wait --verbose --array=4001-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_1_jobs
-example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
- sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y*z*x"]}' --outfile example_2_jobs
+example_2_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py pl_methods.R
+ sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[1], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y*z*x"]}' --outfile example_2_jobs
example_2.feather: example_2_jobs
rm -f example_2.feather
- sbatch --wait --verbose --array=1-$(shell cat example_1_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
-# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_jobs
+ sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_2_jobs
+ sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_2_jobs
+ sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_2_jobs
+ sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_2_jobs
+ sbatch --wait --verbose --array=4001-$(shell cat example_2_jobs | wc -l) run_simulation.sbatch 0 example_2_jobs
+
# example_2_B_jobs: example_2_B.R
# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript example_2_B.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_2_B.feather"]}' --outfile example_2_B_jobs
# rm -f example_2_B.feather
# sbatch --wait --verbose --array=1-3000 run_simulation.sbatch 0 example_2_B_jobs
-example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py
- sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.01],"Bzy":[-0.01],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs
+example_3_jobs: 03_depvar.R simulation_base.R grid_sweep.py pl_methods.R
+ sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 03_depvar.R" --arg_dict '{"N":${Ns},"m":${ms}, "Bxy":[0.7],"Bzy":[-0.7],"seed":${seeds}, "outfile":["example_3.feather"], "y_explained_variance":${explained_variances}}' --outfile example_3_jobs
example_3.feather: example_3_jobs
rm -f example_3.feather
- sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
+ sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_3_jobs
+ sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_3_jobs
+ sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_3_jobs
+ sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_3_jobs
+ sbatch --wait --verbose --array=4001-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 example_3_jobs
-example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py
- sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.01],"Bzy":[-0.01],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "y_explained_variance":${explained_variances}}' --outfile example_4_jobs
+example_4_jobs: 04_depvar_differential.R simulation_base.R grid_sweep.py pl_methods.R
+ sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 04_depvar_differential.R" --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --outfile example_4_jobs
example_4.feather: example_4_jobs
rm -f example_4.feather
- sbatch --wait --verbose --array=1-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs
+ sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_4_jobs
+ sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 example_4_jobs
+ sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 0 example_4_jobs
+ sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 example_4_jobs
+ sbatch --wait --verbose --array=4001-$(shell cat example_4_jobs | wc -l) run_simulation.sbatch 0 example_4_jobs
+
remembr.RDS:example_1.feather example_2.feather example_3.feather example_4.feather plot_example.R plot_dv_example.R summarize_estimator.R
${srun} Rscript plot_dv_example.R --infile example_4.feather --name "plot.df.example.4"
-irr_Ns = ${Ns}
-irr_ms = ${ms}
+irr_Ns = [1000]
+irr_ms = [150,300,600]
irr_seeds=${seeds}
irr_explained_variances=${explained_variances}
+irr_coder_accuracy=[0.80]
-example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py
- sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_5_jobs
+example_5_jobs: 05_irr_indep.R irr_simulation_base.R grid_sweep.py pl_methods.R measerr_methods.R
+ sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 05_irr_indep.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_5.feather"], "y_explained_variance":${irr_explained_variances}, "coder_accuracy":${irr_coder_accuracy}}' --outfile example_5_jobs
example_5.feather:example_5_jobs
rm -f example_5.feather
- sbatch --wait --verbose --array=1-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 0 example_5_jobs
+ sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_5_jobs
+ sbatch --wait --verbose --array=1001-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 1000 example_5_jobs
+ # sbatch --wait --verbose --array=2001-3000 run_simulation.sbatch 2000 example_5_jobs
+ # sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 3000 example_5_jobs
+ # sbatch --wait --verbose --array=2001-$(shell cat example_5_jobs | wc -l) run_simulation.sbatch 4000 example_5_jobs
+
+
+# example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py pl_methods.R
+# sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances},"coder_accuracy":${irr_coder_accuracy}}' --outfile example_6_jobs
-example_6_jobs: 06_irr_dv.R irr_dv_simulation_base.R grid_sweep.py
- sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 06_irr_dv.R" --arg_dict '{"N":${irr_Ns},"m":${irr_ms}, "seed":${irr_seeds}, "outfile":["example_6.feather"], "y_explained_variance":${irr_explained_variances}}' --outfile example_6_jobs
+# example_6.feather:example_6_jobs
+# rm -f example_6.feather
+# sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 example_6_jobs
+# sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 1000 example_6_jobs
+# sbatch --wait --verbose --array=2001-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 2000 example_6_jobs
-example_6.feather:example_6_jobs
- rm -f example_6.feather
- sbatch --wait --verbose --array=1-$(shell cat example_6_jobs | wc -l) run_simulation.sbatch 0 example_6_jobs
-remember_irr.RDS: example_5.feather example_6.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R
+remember_irr.RDS: example_5.feather plot_irr_example.R plot_irr_dv_example.R summarize_estimator.R
rm -f remember_irr.RDS
sbatch --wait --verbose run_job.sbatch Rscript plot_irr_example.R --infile example_5.feather --name "plot.df.example.5"
- sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
+# sbatch --wait --verbose run_job.sbatch Rscript plot_irr_dv_example.R --infile example_6.feather --name "plot.df.example.6"
amelia.ncpus=1)
library(Amelia)
-source("measerr_methods.R") ## for my more generic function.
+source("pl_methods.R")
+source("measerr_methods_2.R") ## for my more generic function.
-run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, rater_formula = y.obs ~ x, proxy_formula = w_pred ~ y){
-
- accuracy <- df[,mean(w_pred==y)]
+run_simulation_depvar <- function(df, result, outcome_formula = y ~ x + z, coder_formulas = list(y.obs.0 ~ 1, y.obs.1 ~ 1), proxy_formula = w_pred ~ y.obs.1+y.obs.0+y){
+ (accuracy <- df[,mean(w_pred==y)])
result <- append(result, list(accuracy=accuracy))
+ (error.cor.z <- cor(df$x, df$w_pred - df$z))
+ (error.cor.x <- cor(df$x, df$w_pred - df$y))
+ (error.cor.y <- cor(df$y, df$y - df$w_pred))
+ result <- append(result, list(error.cor.x = error.cor.x,
+ error.cor.z = error.cor.z,
+ error.cor.y = error.cor.y))
+
+ model.null <- glm(y~1, data=df,family=binomial(link='logit'))
+ (model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
+ (lik.ratio <- exp(logLik(model.true) - logLik(model.null)))
- (model.true <- glm(y ~ x + z, data=df, family=binomial(link='logit')))
true.ci.Bxy <- confint(model.true)['x',]
true.ci.Bzy <- confint(model.true)['z',]
+
+ result <- append(result, list(lik.ratio=lik.ratio))
+
result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
Bzy.est.true=coef(model.true)['z'],
Bxy.ci.upper.true = true.ci.Bxy[2],
Bxy.ci.lower.true = true.ci.Bxy[1],
Bzy.ci.upper.true = true.ci.Bzy[2],
Bzy.ci.lower.true = true.ci.Bzy[1]))
+
+ (model.naive <- lm(y~w_pred+z, data=df))
+
+ naive.ci.Bxy <- confint(model.naive)['w_pred',]
+ naive.ci.Bzy <- confint(model.naive)['z',]
+
+ result <- append(result, list(Bxy.est.naive=coef(model.naive)['w_pred'],
+ Bzy.est.naive=coef(model.naive)['z'],
+ Bxy.ci.upper.naive = naive.ci.Bxy[2],
+ Bxy.ci.lower.naive = naive.ci.Bxy[1],
+ Bzy.ci.upper.naive = naive.ci.Bzy[2],
+ Bzy.ci.lower.naive = naive.ci.Bzy[1]))
Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1]))
- df.loa0.mle <- copy(df)
- df.loa0.mle[,y:=y.obs.0]
- loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
- fisher.info <- solve(loa0.mle$hessian)
- coef <- loa0.mle$par
- ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
- ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+ ## df.loa0.mle <- copy(df)
+ ## df.loa0.mle[,y:=y.obs.0]
+ ## loa0.mle <- measerr_mle_dv(df.loa0.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+ ## fisher.info <- solve(loa0.mle$hessian)
+ ## coef <- loa0.mle$par
+ ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
- result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
- Bzy.est.loa0.mle=coef['z'],
- Bxy.ci.upper.loa0.mle = ci.upper['x'],
- Bxy.ci.lower.loa0.mle = ci.lower['x'],
- Bzy.ci.upper.loa0.mle = ci.upper['z'],
- Bzy.ci.lower.loa0.mle = ci.upper['z']))
+ ## result <- append(result, list(Bxy.est.loa0.mle=coef['x'],
+ ## Bzy.est.loa0.mle=coef['z'],
+ ## Bxy.ci.upper.loa0.mle = ci.upper['x'],
+ ## Bxy.ci.lower.loa0.mle = ci.lower['x'],
+ ## Bzy.ci.upper.loa0.mle = ci.upper['z'],
+ ## Bzy.ci.lower.loa0.mle = ci.upper['z']))
loco.feasible <- glm(y.obs.0 ~ x + z, data = df[(!is.na(y.obs.0)) & (y.obs.1 == y.obs.0)], family=binomial(link='logit'))
Bzy.ci.upper.loco.feasible = loco.feasible.ci.Bzy[2],
Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+
+ ## df.double.proxy <- copy(df)
+ ## df.double.proxy <- df.double.proxy[,y.obs:=NA]
+ ## df.double.proxy <- df.double.proxy[,y:=NA]
+
+ ## double.proxy.mle <- measerr_irr_mle_dv(df.double.proxy, outcome_formula=y~x+z, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0 ~ y), proxy_formula=w_pred ~ y.obs.0 + y, proxy_family=binomial(link='logit'))
+ ## print(double.proxy.mle$hessian)
+ ## fisher.info <- solve(double.proxy.mle$hessian)
+ ## coef <- double.proxy.mle$par
+ ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ ## result <- append(result, list(Bxy.est.double.proxy=coef['x'],
+ ## Bzy.est.double.proxy=coef['z'],
+ ## Bxy.ci.upper.double.proxy = ci.upper['x'],
+ ## Bxy.ci.lower.double.proxy = ci.lower['x'],
+ ## Bzy.ci.upper.double.proxy = ci.upper['z'],
+ ## Bzy.ci.lower.double.proxy = ci.lower['z']))
+
- df.loco.mle <- copy(df)
- df.loco.mle[,y.obs:=NA]
- df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0]
- df.loco.mle[,y.true:=y]
- df.loco.mle[,y:=y.obs]
- print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)])
- loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
- fisher.info <- solve(loco.mle$hessian)
- coef <- loco.mle$par
+ df.triple.proxy <- copy(df)
+ df.triple.proxy <- df.triple.proxy[,y.obs:=NA]
+ df.triple.proxy <- df.triple.proxy[,y:=NA]
+
+ triple.proxy.mle <- measerr_irr_mle_dv(df.triple.proxy, outcome_formula=outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=coder_formulas, proxy_formula=proxy_formula, proxy_family=binomial(link='logit'))
+ print(triple.proxy.mle$hessian)
+ fisher.info <- solve(triple.proxy.mle$hessian)
+ print(fisher.info)
+ coef <- triple.proxy.mle$par
ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
- result <- append(result, list(Bxy.est.loco.mle=coef['x'],
- Bzy.est.loco.mle=coef['z'],
- Bxy.ci.upper.loco.mle = ci.upper['x'],
- Bxy.ci.lower.loco.mle = ci.lower['x'],
- Bzy.ci.upper.loco.mle = ci.upper['z'],
- Bzy.ci.lower.loco.mle = ci.lower['z']))
+ result <- append(result, list(Bxy.est.triple.proxy=coef['x'],
+ Bzy.est.triple.proxy=coef['z'],
+ Bxy.ci.upper.triple.proxy = ci.upper['x'],
+ Bxy.ci.lower.triple.proxy = ci.lower['x'],
+ Bzy.ci.upper.triple.proxy = ci.upper['z'],
+ Bzy.ci.lower.triple.proxy = ci.lower['z']))
+
+ ## df.loco.mle <- copy(df)
+ ## df.loco.mle[,y.obs:=NA]
+ ## df.loco.mle[(y.obs.0)==(y.obs.1),y.obs:=y.obs.0]
+ ## df.loco.mle[,y.true:=y]
+ ## df.loco.mle[,y:=y.obs]
+ ## print(df.loco.mle[!is.na(y.obs.1),mean(y.true==y,na.rm=TRUE)])
+ ## loco.mle <- measerr_mle_dv(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula)
+ ## fisher.info <- solve(loco.mle$hessian)
+ ## coef <- loco.mle$par
+ ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ ## result <- append(result, list(Bxy.est.loco.mle=coef['x'],
+ ## Bzy.est.loco.mle=coef['z'],
+ ## Bxy.ci.upper.loco.mle = ci.upper['x'],
+ ## Bxy.ci.lower.loco.mle = ci.lower['x'],
+ ## Bzy.ci.upper.loco.mle = ci.upper['z'],
+ ## Bzy.ci.lower.loco.mle = ci.lower['z']))
+
+
- print(rater_formula)
- print(proxy_formula)
+ ## my implementatoin of liklihood based correction
+ mod.zhang <- zhang.mle.dv(df.loco.mle)
+ coef <- coef(mod.zhang)
+ ci <- confint(mod.zhang,method='quad')
+
+ result <- append(result,
+ list(Bxy.est.zhang = coef['Bxy'],
+ Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
+ Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
+ Bzy.est.zhang = coef['Bzy'],
+ Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
+ Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
+
+
+ print(df.loco.mle)
+
+ # amelia says use normal distribution for binary variables.
+ tryCatch({
+ amelia.out.k <- amelia(df.loco.mle, m=200, p2s=0, idvars=c('y','ystar','w','y.obs.1','y.obs.0','y.true'))
+ mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+ (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
+ est.x.mi <- coefse['x','Estimate']
+ est.x.se <- coefse['x','Std.Error']
+ result <- append(result,
+ list(Bxy.est.amelia.full = est.x.mi,
+ Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
+ Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se
+ ))
+
+ est.z.mi <- coefse['z','Estimate']
+ est.z.se <- coefse['z','Std.Error']
+
+ result <- append(result,
+ list(Bzy.est.amelia.full = est.z.mi,
+ Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se,
+ Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
+ ))
+
+ },
+ error = function(e){
+ message("An error occurred:\n",e)
+ result$error <- paste0(result$error,'\n', e)
+ })
## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
## fisher.info <- solve(mle.irr$hessian)
options(amelia.parallel="no",
amelia.ncpus=1)
library(Amelia)
+source("measerr_methods.R")
+source("pl_methods.R")
-source("measerr_methods.R") ## for my more generic function.
-
-run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, truth_formula = x ~ z){
+run_simulation <- function(df, result, outcome_formula = y ~ x + z, proxy_formula = w_pred ~ x, coder_formulas=list(x.obs.1~x, x.obs.0~x), truth_formula = x ~ z){
accuracy <- df[,mean(w_pred==x)]
result <- append(result, list(accuracy=accuracy))
+
+
loa0.feasible <- lm(y ~ x.obs.0 + z, data = df[!(is.na(x.obs.1))])
loa0.ci.Bxy <- confint(loa0.feasible)['x.obs.0',]
Bxy.ci.lower.loa0.feasible = loa0.ci.Bxy[1],
Bzy.ci.upper.loa0.feasible = loa0.ci.Bzy[2],
Bzy.ci.lower.loa0.feasible = loa0.ci.Bzy[1]))
-
+ print("fitting loa0 model")
df.loa0.mle <- copy(df)
df.loa0.mle[,x:=x.obs.0]
Bzy.ci.upper.loa0.mle = ci.upper['z'],
Bzy.ci.lower.loa0.mle = ci.upper['z']))
+
+
loco.feasible <- lm(y ~ x.obs.1 + z, data = df[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0)])
+
loco.feasible.ci.Bxy <- confint(loco.feasible)['x.obs.1',]
loco.feasible.ci.Bzy <- confint(loco.feasible)['z',]
Bzy.ci.lower.loco.feasible = loco.feasible.ci.Bzy[1]))
+ (model.naive <- lm(y~w_pred+z, data=df))
+
+ naive.ci.Bxy <- confint(model.naive)['w_pred',]
+ naive.ci.Bzy <- confint(model.naive)['z',]
+
+ result <- append(result, list(Bxy.est.naive=coef(model.naive)['w_pred'],
+ Bzy.est.naive=coef(model.naive)['z'],
+ Bxy.ci.upper.naive = naive.ci.Bxy[2],
+ Bxy.ci.lower.naive = naive.ci.Bxy[1],
+ Bzy.ci.upper.naive = naive.ci.Bzy[2],
+ Bzy.ci.lower.naive = naive.ci.Bzy[1]))
+
+ print("fitting loco model")
+
df.loco.mle <- copy(df)
df.loco.mle[,x.obs:=NA]
df.loco.mle[(x.obs.0)==(x.obs.1),x.obs:=x.obs.0]
df.loco.mle[,x.true:=x]
df.loco.mle[,x:=x.obs]
print(df.loco.mle[!is.na(x.obs.1),mean(x.true==x,na.rm=TRUE)])
+ loco.accuracy <- df.loco.mle[(!is.na(x.obs.1)) & (x.obs.1 == x.obs.0),mean(x.obs.1 == x.true)]
loco.mle <- measerr_mle(df.loco.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
fisher.info <- solve(loco.mle$hessian)
coef <- loco.mle$par
ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
- result <- append(result, list(Bxy.est.loco.mle=coef['x'],
+ result <- append(result, list(loco.accuracy=loco.accuracy,
+ Bxy.est.loco.mle=coef['x'],
Bzy.est.loco.mle=coef['z'],
Bxy.ci.upper.loco.mle = ci.upper['x'],
Bxy.ci.lower.loco.mle = ci.lower['x'],
Bzy.ci.upper.loco.mle = ci.upper['z'],
Bzy.ci.lower.loco.mle = ci.lower['z']))
- ## print(rater_formula)
- ## print(proxy_formula)
- ## mle.irr <- measerr_irr_mle( df, outcome_formula = outcome_formula, rater_formula = rater_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
+ df.double.proxy.mle <- copy(df)
+ df.double.proxy.mle[,x.obs:=NA]
+ print("fitting double proxy model")
+
+ double.proxy.mle <- measerr_irr_mle(df.double.proxy.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, coder_formulas=coder_formulas[1], truth_formula=truth_formula)
+ fisher.info <- solve(double.proxy.mle$hessian)
+ coef <- double.proxy.mle$par
+ ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ result <- append(result, list(
+ Bxy.est.double.proxy=coef['x'],
+ Bzy.est.double.proxy=coef['z'],
+ Bxy.ci.upper.double.proxy = ci.upper['x'],
+ Bxy.ci.lower.double.proxy = ci.lower['x'],
+ Bzy.ci.upper.double.proxy = ci.upper['z'],
+ Bzy.ci.lower.double.proxy = ci.lower['z']))
+
+ df.triple.proxy.mle <- copy(df)
+ df.triple.proxy.mle[,x.obs:=NA]
+
+ print("fitting triple proxy model")
+ triple.proxy.mle <- measerr_irr_mle(df.triple.proxy.mle, outcome_formula=outcome_formula, proxy_formula=proxy_formula, coder_formulas=coder_formulas, truth_formula=truth_formula)
+ fisher.info <- solve(triple.proxy.mle$hessian)
+ coef <- triple.proxy.mle$par
+ ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
+ ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
+
+ result <- append(result, list(
+ Bxy.est.triple.proxy=coef['x'],
+ Bzy.est.triple.proxy=coef['z'],
+ Bxy.ci.upper.triple.proxy = ci.upper['x'],
+ Bxy.ci.lower.triple.proxy = ci.lower['x'],
+ Bzy.ci.upper.triple.proxy = ci.upper['z'],
+ Bzy.ci.lower.triple.proxy = ci.lower['z']))
+ tryCatch({
+ amelia.out.k <- amelia(df.loco.mle, m=200, p2s=0, idvars=c('x.true','w','x.obs.1','x.obs.0','x'))
+ mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+ (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
+
+ est.x.mi <- coefse['x.obs','Estimate']
+ est.x.se <- coefse['x.obs','Std.Error']
+ result <- append(result,
+ list(Bxy.est.amelia.full = est.x.mi,
+ Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
+ Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se
+ ))
+
+ est.z.mi <- coefse['z','Estimate']
+ est.z.se <- coefse['z','Std.Error']
+
+ result <- append(result,
+ list(Bzy.est.amelia.full = est.z.mi,
+ Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se,
+ Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
+ ))
+
+ },
+ error = function(e){
+ message("An error occurred:\n",e)
+ result$error <-paste0(result$error,'\n', e)
+ }
+ )
+
+ tryCatch({
+
+ mod.zhang.lik <- zhang.mle.iv(df.loco.mle)
+ coef <- coef(mod.zhang.lik)
+ ci <- confint(mod.zhang.lik,method='quad')
+ result <- append(result,
+ list(Bxy.est.zhang = coef['Bxy'],
+ Bxy.ci.upper.zhang = ci['Bxy','97.5 %'],
+ Bxy.ci.lower.zhang = ci['Bxy','2.5 %'],
+ Bzy.est.zhang = coef['Bzy'],
+ Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
+ Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
+ },
+
+ error = function(e){
+ message("An error occurred:\n",e)
+ result$error <- paste0(result$error,'\n', e)
+ })
+
+ df <- df.loco.mle
+ N <- nrow(df)
+ m <- nrow(df[!is.na(x.obs)])
+ p <- v <- train <- rep(0,N)
+ M <- m
+ p[(M+1):(N)] <- 1
+ v[1:(M)] <- 1
+ df <- df[order(x.obs)]
+ y <- df[,y]
+ x <- df[,x.obs]
+ z <- df[,z]
+ w <- df[,w_pred]
+ # gmm gets pretty close
+ (gmm.res <- predicted_covariates(y, x, z, w, v, train, p, max_iter=100, verbose=TRUE))
+
+ result <- append(result,
+ list(Bxy.est.gmm = gmm.res$beta[1,1],
+ Bxy.ci.upper.gmm = gmm.res$confint[1,2],
+ Bxy.ci.lower.gmm = gmm.res$confint[1,1],
+ gmm.ER_pval = gmm.res$ER_pval
+ ))
+
+ result <- append(result,
+ list(Bzy.est.gmm = gmm.res$beta[2,1],
+ Bzy.ci.upper.gmm = gmm.res$confint[2,2],
+ Bzy.ci.lower.gmm = gmm.res$confint[2,1]))
+
- ## fisher.info <- solve(mle.irr$hessian)
- ## coef <- mle.irr$par
- ## ci.upper <- coef + sqrt(diag(fisher.info)) * 1.96
- ## ci.lower <- coef - sqrt(diag(fisher.info)) * 1.96
-
- ## result <- append(result,
- ## list(Bxy.est.mle = coef['x'],
- ## Bxy.ci.upper.mle = ci.upper['x'],
- ## Bxy.ci.lower.mle = ci.lower['x'],
- ## Bzy.est.mle = coef['z'],
- ## Bzy.ci.upper.mle = ci.upper['z'],
- ## Bzy.ci.lower.mle = ci.lower['z']))
return(result)
library(formula.tools)
library(matrixStats)
+library(optimx)
library(bbmle)
## df: dataframe to model
## outcome_formula: formula for y | x, z
return(fit)
}
-## Experimental, and not necessary if errors are independent.
-measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), rater_formula, proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
- ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it.
+measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
- ## probability of y given observed data.
- df.obs <- df[!is.na(x.obs.1)]
+ df.obs <- model.frame(outcome_formula, df)
+ response.var <- all.vars(outcome_formula)[1]
proxy.variable <- all.vars(proxy_formula)[1]
- df.x.obs.1 <- copy(df.obs)[,x:=1]
- df.x.obs.0 <- copy(df.obs)[,x:=0]
- y.obs <- df.obs[,y]
-
- nll <- function(params){
- outcome.model.matrix.x.obs.0 <- model.matrix(outcome_formula, df.x.obs.0)
- outcome.model.matrix.x.obs.1 <- model.matrix(outcome_formula, df.x.obs.1)
-
- param.idx <- 1
- n.outcome.model.covars <- dim(outcome.model.matrix.x.obs.0)[2]
- outcome.params <- params[param.idx:n.outcome.model.covars]
- param.idx <- param.idx + n.outcome.model.covars
-
- sigma.y <- params[param.idx]
- param.idx <- param.idx + 1
-
- ll.y.x.obs.0 <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix.x.obs.0),sd=sigma.y, log=TRUE)
- ll.y.x.obs.1 <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix.x.obs.1),sd=sigma.y, log=TRUE)
-
- ## assume that the two coders are statistically independent conditional on x
- ll.x.obs.0.x0 <- vector(mode='numeric', length=nrow(df.obs))
- ll.x.obs.1.x0 <- vector(mode='numeric', length=nrow(df.obs))
- ll.x.obs.0.x1 <- vector(mode='numeric', length=nrow(df.obs))
- ll.x.obs.1.x1 <- vector(mode='numeric', length=nrow(df.obs))
-
- rater.model.matrix.x.obs.0 <- model.matrix(rater_formula, df.x.obs.0)
- rater.model.matrix.x.obs.1 <- model.matrix(rater_formula, df.x.obs.1)
-
- n.rater.model.covars <- dim(rater.model.matrix.x.obs.0)[2]
- rater.0.params <- params[param.idx:(n.rater.model.covars + param.idx - 1)]
- param.idx <- param.idx + n.rater.model.covars
-
- rater.1.params <- params[param.idx:(n.rater.model.covars + param.idx - 1)]
- param.idx <- param.idx + n.rater.model.covars
-
- # probability of rater 0 if x is 0 or 1
- ll.x.obs.0.x0[df.obs$x.obs.0==1] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==1,]), log=TRUE)
- ll.x.obs.0.x0[df.obs$x.obs.0==0] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.0==0,]), log=TRUE, lower.tail=FALSE)
- ll.x.obs.0.x1[df.obs$x.obs.0==1] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.0==1,]), log=TRUE)
- ll.x.obs.0.x1[df.obs$x.obs.0==0] <- plogis(rater.0.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.0==0,]), log=TRUE, lower.tail=FALSE)
-
- # probability of rater 1 if x is 0 or 1
- ll.x.obs.1.x0[df.obs$x.obs.1==1] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.1==1,]), log=TRUE)
- ll.x.obs.1.x0[df.obs$x.obs.1==0] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.0[df.obs$x.obs.1==0,]), log=TRUE, lower.tail=FALSE)
- ll.x.obs.1.x1[df.obs$x.obs.1==1] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.1==1,]), log=TRUE)
- ll.x.obs.1.x1[df.obs$x.obs.1==0] <- plogis(rater.1.params %*% t(rater.model.matrix.x.obs.1[df.obs$x.obs.1==0,]), log=TRUE, lower.tail=FALSE)
-
- proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.x.obs.0)
- proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.x.obs.1)
-
- n.proxy.model.covars <- dim(proxy.model.matrix.x0)[2]
- proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)]
- param.idx <- param.idx + n.proxy.model.covars
-
- proxy.obs <- with(df.obs, eval(parse(text=proxy.variable)))
-
- if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
- ll.w.obs.x0 <- vector(mode='numeric',length=dim(proxy.model.matrix.x0)[1])
- ll.w.obs.x1 <- vector(mode='numeric',length=dim(proxy.model.matrix.x1)[1])
-
- # proxy_formula likelihood using logistic regression
- ll.w.obs.x0[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.obs==1,]),log=TRUE)
- ll.w.obs.x0[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
-
- ll.w.obs.x1[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.obs==1,]),log=TRUE)
- ll.w.obs.x1[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
- }
-
- ## assume that the probability of x is a logistic regression depending on z
- truth.model.matrix.obs <- model.matrix(truth_formula, df.obs)
- n.truth.params <- dim(truth.model.matrix.obs)[2]
- truth.params <- params[param.idx:(n.truth.params + param.idx - 1)]
-
- ll.obs.x0 <- plogis(truth.params %*% t(truth.model.matrix.obs), log=TRUE)
- ll.obs.x1 <- plogis(truth.params %*% t(truth.model.matrix.obs), log=TRUE, lower.tail=FALSE)
-
- ll.obs <- colLogSumExps(rbind(ll.y.x.obs.0 + ll.x.obs.0.x0 + ll.x.obs.1.x0 + ll.obs.x0 + ll.w.obs.x0,
- ll.y.x.obs.1 + ll.x.obs.0.x1 + ll.x.obs.1.x1 + ll.obs.x1 + ll.w.obs.x1))
-
- ### NOW FOR THE FUN PART. Likelihood of the unobserved data.
- ### we have to integrate out x.obs.0, x.obs.1, and x.
-
-
- ## THE OUTCOME
- df.unobs <- df[is.na(x.obs)]
- df.x.unobs.0 <- copy(df.unobs)[,x:=0]
- df.x.unobs.1 <- copy(df.unobs)[,x:=1]
- y.unobs <- df.unobs$y
-
- outcome.model.matrix.x.unobs.0 <- model.matrix(outcome_formula, df.x.unobs.0)
- outcome.model.matrix.x.unobs.1 <- model.matrix(outcome_formula, df.x.unobs.1)
-
- ll.y.unobs.x0 <- dnorm(y.unobs, outcome.params %*% t(outcome.model.matrix.x.unobs.0), sd=sigma.y, log=TRUE)
- ll.y.unobs.x1 <- dnorm(y.unobs, outcome.params %*% t(outcome.model.matrix.x.unobs.1), sd=sigma.y, log=TRUE)
-
-
- ## THE UNLABELED DATA
-
-
- ## assume that the two coders are statistically independent conditional on x
- ll.x.unobs.0.x0 <- vector(mode='numeric', length=nrow(df.unobs))
- ll.x.unobs.1.x0 <- vector(mode='numeric', length=nrow(df.unobs))
- ll.x.unobs.0.x1 <- vector(mode='numeric', length=nrow(df.unobs))
- ll.x.unobs.1.x1 <- vector(mode='numeric', length=nrow(df.unobs))
-
- df.x.unobs.0[,x.obs := 1]
- df.x.unobs.1[,x.obs := 1]
-
- rater.model.matrix.x.unobs.0 <- model.matrix(rater_formula, df.x.unobs.0)
- rater.model.matrix.x.unobs.1 <- model.matrix(rater_formula, df.x.unobs.1)
-
-
- ## # probability of rater 0 if x is 0 or 1
- ## ll.x.unobs.0.x0 <- colLogSumExps(rbind(plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE),
- ## plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE, lower.tail=TRUE)))
-
- ## ll.x.unobs.0.x1 <- colLogSumExps(rbind(plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE),
- ## plogis(rater.0.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE, lower.tail=TRUE)))
-
- ## # probability of rater 1 if x is 0 or 1
- ## ll.x.unobs.1.x0 <- colLogSumExps(rbind(plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE),
- ## plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.0), log=TRUE, lower.tail=TRUE)))
-
- ## ll.x.unobs.1.x1 <- colLogSumExps(rbind(plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE),
- ## plogis(rater.1.params %*% t(rater.model.matrix.x.unobs.1), log=TRUE, lower.tail=TRUE)))
-
-
- proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable)))
- proxy.model.matrix.x0.unobs <- model.matrix(proxy_formula, df.x.unobs.0)
- proxy.model.matrix.x1.unobs <- model.matrix(proxy_formula, df.x.unobs.1)
-
- if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
- ll.w.unobs.x0 <- vector(mode='numeric',length=dim(proxy.model.matrix.x0)[1])
- ll.w.unobs.x1 <- vector(mode='numeric',length=dim(proxy.model.matrix.x1)[1])
-
-
- # proxy_formula likelihood using logistic regression
- ll.w.unobs.x0[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0.unobs[proxy.unobs==1,]),log=TRUE)
- ll.w.unobs.x0[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0.unobs[proxy.unobs==0,]),log=TRUE, lower.tail=FALSE)
-
- ll.w.unobs.x1[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1.unobs[proxy.unobs==1,]),log=TRUE)
- ll.w.unobs.x1[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1.unobs[proxy.unobs==0,]),log=TRUE, lower.tail=FALSE)
- }
-
- truth.model.matrix.unobs <- model.matrix(truth_formula, df.unobs)
-
- ll.unobs.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE)
- ll.unobs.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE)
-
- ll.unobs <- colLogSumExps(rbind(ll.unobs.x0 + ll.w.unobs.x0 + ll.y.unobs.x0,
- ll.unobs.x1 + ll.w.unobs.x1 + ll.y.unobs.x1))
-
- return(-1 *( sum(ll.obs) + sum(ll.unobs)))
- }
-
- outcome.params <- colnames(model.matrix(outcome_formula,df))
- lower <- rep(-Inf, length(outcome.params))
-
- if(outcome_family$family=='gaussian'){
- params <- c(outcome.params, 'sigma_y')
- lower <- c(lower, 0.00001)
- } else {
- params <- outcome.params
- }
-
- rater.0.params <- colnames(model.matrix(rater_formula,df))
- params <- c(params, paste0('rater_0',rater.0.params))
- lower <- c(lower, rep(-Inf, length(rater.0.params)))
-
- rater.1.params <- colnames(model.matrix(rater_formula,df))
- params <- c(params, paste0('rater_1',rater.1.params))
- lower <- c(lower, rep(-Inf, length(rater.1.params)))
-
- proxy.params <- colnames(model.matrix(proxy_formula, df))
- params <- c(params, paste0('proxy_',proxy.params))
- lower <- c(lower, rep(-Inf, length(proxy.params)))
-
- truth.params <- colnames(model.matrix(truth_formula, df))
- params <- c(params, paste0('truth_', truth.params))
- lower <- c(lower, rep(-Inf, length(truth.params)))
- start <- rep(0.1,length(params))
- names(start) <- params
-
-
- if(method=='optim'){
- fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
- } else {
-
- quoted.names <- gsub("[\\(\\)]",'',names(start))
- print(quoted.names)
- text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}")
-
- measerr_mle_nll <- eval(parse(text=text))
- names(start) <- quoted.names
- names(lower) <- quoted.names
- fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
- }
-
- return(fit)
-}
-
-
-measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
+ truth.variable <- all.vars(truth_formula)[1]
+ outcome.model.matrix <- model.matrix(outcome_formula, df)
+ proxy.model.matrix <- model.matrix(proxy_formula, df)
+ y.obs <- with(df.obs,eval(parse(text=response.var)))
measerr_mle_nll <- function(params){
- df.obs <- model.frame(outcome_formula, df)
- proxy.variable <- all.vars(proxy_formula)[1]
- proxy.model.matrix <- model.matrix(proxy_formula, df)
- response.var <- all.vars(outcome_formula)[1]
- y.obs <- with(df.obs,eval(parse(text=response.var)))
-
- outcome.model.matrix <- model.matrix(outcome_formula, df)
-
param.idx <- 1
n.outcome.model.covars <- dim(outcome.model.matrix)[2]
outcome.params <- params[param.idx:n.outcome.model.covars]
if(outcome_family$family == "gaussian"){
sigma.y <- params[param.idx]
param.idx <- param.idx + 1
-
# outcome_formula likelihood using linear regression
ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE)
}
}
df.obs <- model.frame(truth_formula, df)
- truth.variable <- all.vars(truth_formula)[1]
+
truth.obs <- with(df.obs, eval(parse(text=truth.variable)))
truth.model.matrix <- model.matrix(truth_formula,df)
n.truth.model.covars <- dim(truth.model.matrix)[2]
return(fit)
}
+## Experimental, but probably works.
+measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), coder_formulas=list(x.obs.0~x, x.obs.1~x), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
+
+ ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it.
+ # this time we never get to observe the true X
+ outcome.model.matrix <- model.matrix(outcome_formula, df)
+ proxy.model.matrix <- model.matrix(proxy_formula, df)
+ response.var <- all.vars(outcome_formula)[1]
+ proxy.var <- all.vars(proxy_formula)[1]
+ param.var <- all.vars(truth_formula)[1]
+ truth.var<- all.vars(truth_formula)[1]
+ y <- with(df,eval(parse(text=response.var)))
+
+ nll <- function(params){
+ param.idx <- 1
+ n.outcome.model.covars <- dim(outcome.model.matrix)[2]
+ outcome.params <- params[param.idx:n.outcome.model.covars]
+ param.idx <- param.idx + n.outcome.model.covars
+
+
+ if(outcome_family$family == "gaussian"){
+ sigma.y <- params[param.idx]
+ param.idx <- param.idx + 1
+ }
+
+
+ n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+ proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)]
+ param.idx <- param.idx + n.proxy.model.covars
+
+ df.temp <- copy(df)
+
+ if((truth_family$family == "binomial")
+ & (truth_family$link=='logit')){
+ integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE))
+ ll.parts <- matrix(nrow=nrow(df),ncol=nrow(integrate.grid))
+ for(i in 1:nrow(integrate.grid)){
+ # setup the dataframe for this row
+ row <- integrate.grid[i,]
+
+ df.temp[[param.var]] <- row[[1]]
+ ci <- 2
+ for(coder_formula in coder_formulas){
+ coder.var <- all.vars(coder_formula)[1]
+ df.temp[[coder.var]] <- row[[ci]]
+ ci <- ci + 1
+ }
+
+ outcome.model.matrix <- model.matrix(outcome_formula, df.temp)
+ if(outcome_family$family == "gaussian"){
+ ll.y <- dnorm(y, outcome.params %*% t(outcome.model.matrix), sd=sigma.y, log=TRUE)
+ }
+
+ if(proxy_family$family=="binomial" & (proxy_family$link=='logit')){
+ proxy.model.matrix <- model.matrix(proxy_formula, df.temp)
+ ll.w <- vector(mode='numeric', length=dim(proxy.model.matrix)[1])
+ proxyvar <- with(df.temp,eval(parse(text=proxy.var)))
+ ll.w[proxyvar==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==1,]),log=TRUE)
+ ll.w[proxyvar==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==0,]),log=TRUE,lower.tail=FALSE)
+ }
+
+ ## probability of the coded variables
+ coder.lls <- matrix(nrow=nrow(df.temp),ncol=length(coder_formulas))
+ ci <- 1
+ for(coder_formula in coder_formulas){
+ coder.model.matrix <- model.matrix(coder_formula, df.temp)
+ n.coder.model.covars <- dim(coder.model.matrix)[2]
+ coder.params <- params[param.idx:(n.coder.model.covars + param.idx - 1)]
+ param.idx <- param.idx + n.coder.model.covars
+ coder.var <- all.vars(coder_formula)[1]
+ x.obs <- with(df.temp, eval(parse(text=coder.var)))
+ true.codervar <- df[[all.vars(coder_formula)[1]]]
+
+ ll.coder <- vector(mode='numeric', length=dim(coder.model.matrix)[1])
+ ll.coder[x.obs==1] <- plogis(coder.params %*% t(coder.model.matrix[x.obs==1,]),log=TRUE)
+ ll.coder[x.obs==0] <- plogis(coder.params %*% t(coder.model.matrix[x.obs==0,]),log=TRUE,lower.tail=FALSE)
+
+ # don't count when we know the observed value, unless we're accounting for observed value
+ ll.coder[(!is.na(true.codervar)) & (true.codervar != x.obs)] <- NA
+ coder.lls[,ci] <- ll.coder
+ ci <- ci + 1
+ }
+
+ truth.model.matrix <- model.matrix(truth_formula, df.temp)
+ n.truth.model.covars <- dim(truth.model.matrix)[2]
+ truth.params <- params[param.idx:(n.truth.model.covars + param.idx - 1)]
+
+ for(coder_formula in coder_formulas){
+ coder.model.matrix <- model.matrix(coder_formula, df.temp)
+ n.coder.model.covars <- dim(coder.model.matrix)[2]
+ param.idx <- param.idx - n.coder.model.covars
+ }
+
+ x <- with(df.temp, eval(parse(text=truth.var)))
+ ll.truth <- vector(mode='numeric', length=dim(truth.model.matrix)[1])
+ ll.truth[x==1] <- plogis(truth.params %*% t(truth.model.matrix[x==1,]), log=TRUE)
+ ll.truth[x==0] <- plogis(truth.params %*% t(truth.model.matrix[x==0,]), log=TRUE, lower.tail=FALSE)
+
+ true.truthvar <- df[[all.vars(truth_formula)[1]]]
+
+ if(!is.null(true.truthvar)){
+ # ll.truth[(!is.na(true.truthvar)) & (true.truthvar != truthvar)] <- -Inf
+ # ll.truth[(!is.na(true.truthvar)) & (true.truthvar == truthvar)] <- 0
+ }
+ ll.parts[,i] <- ll.y + ll.w + apply(coder.lls,1,sum) + ll.truth
+
+ }
+
+ lls <- rowLogSumExps(ll.parts,na.rm=TRUE)
+
+ ## likelihood of observed data
+ target <- -1 * sum(lls)
+ return(target)
+ }
+ }
+
+ outcome.params <- colnames(model.matrix(outcome_formula,df))
+ lower <- rep(-Inf, length(outcome.params))
+
+ if(outcome_family$family=='gaussian'){
+ params <- c(outcome.params, 'sigma_y')
+ lower <- c(lower, 0.00001)
+ } else {
+ params <- outcome.params
+ }
+
+ proxy.params <- colnames(model.matrix(proxy_formula, df))
+ params <- c(params, paste0('proxy_',proxy.params))
+ positive.params <- paste0('proxy_',truth.var)
+ lower <- c(lower, rep(-Inf, length(proxy.params)))
+ names(lower) <- params
+ lower[positive.params] <- 0.01
+ ci <- 0
+
+ for(coder_formula in coder_formulas){
+ coder.params <- colnames(model.matrix(coder_formula,df))
+ params <- c(params, paste0('coder_',ci,coder.params))
+ positive.params <- paste0('coder_', ci, truth.var)
+ ci <- ci + 1
+ lower <- c(lower, rep(-Inf, length(coder.params)))
+ names(lower) <- params
+ lower[positive.params] <- 0.01
+ }
+
+ truth.params <- colnames(model.matrix(truth_formula, df))
+ params <- c(params, paste0('truth_', truth.params))
+ lower <- c(lower, rep(-Inf, length(truth.params)))
+ start <- rep(0.1,length(params))
+ names(start) <- params
+ names(lower) <- params
+
+ if(method=='optim'){
+ print(start)
+ fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+ } else {
+
+ quoted.names <- gsub("[\\(\\)]",'',names(start))
+ print(quoted.names)
+ text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}")
+
+ measerr_mle_nll <- eval(parse(text=text))
+ names(start) <- quoted.names
+ names(lower) <- quoted.names
+ fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, method='L-BFGS-B',control=list(maxit=1e6))
+ }
+
+ return(fit)
+}
+
+## Experimental, and does not work.
+measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0~y+w_pred+y.obs.1,y.obs.1~y+w_pred+y.obs.0), proxy_formula=w_pred~y, proxy_family=binomial(link='logit'),method='optim'){
+ integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE))
+ print(integrate.grid)
+
+
+ outcome.model.matrix <- model.matrix(outcome_formula, df)
+ n.outcome.model.covars <- dim(outcome.model.matrix)[2]
+
+
+ ### in this scenario, the ground truth also has measurement error, but we have repeated measures for it.
+ # this time we never get to observe the true X
+ nll <- function(params){
+ param.idx <- 1
+ outcome.params <- params[param.idx:n.outcome.model.covars]
+ param.idx <- param.idx + n.outcome.model.covars
+ proxy.model.matrix <- model.matrix(proxy_formula, df)
+ n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+ response.var <- all.vars(outcome_formula)[1]
+
+ if(outcome_family$family == "gaussian"){
+ sigma.y <- params[param.idx]
+ param.idx <- param.idx + 1
+ }
+
+ proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)]
+ param.idx <- param.idx + n.proxy.model.covars
+
+ df.temp <- copy(df)
+
+ if((outcome_family$family == "binomial")
+ & (outcome_family$link=='logit')){
+ ll.parts <- matrix(nrow=nrow(df),ncol=nrow(integrate.grid))
+ for(i in 1:nrow(integrate.grid)){
+ # setup the dataframe for this row
+ row <- integrate.grid[i,]
+
+ df.temp[[response.var]] <- row[[1]]
+ ci <- 2
+ for(coder_formula in coder_formulas){
+ codervar <- all.vars(coder_formula)[1]
+ df.temp[[codervar]] <- row[[ci]]
+ ci <- ci + 1
+ }
+
+ outcome.model.matrix <- model.matrix(outcome_formula, df.temp)
+ if(outcome_family$family == "gaussian"){
+ ll.y <- dnorm(df.temp[[response.var]], outcome.params %*% t(outcome.model.matrix), sd=sigma.y, log=T)
+ }
+
+ if(outcome_family$family == "binomial" & (outcome_family$link=='logit')){
+ ll.y <- vector(mode='numeric',length=nrow(df.temp))
+ ll.y[df.temp[[response.var]]==1] <- plogis( outcome.params %*% t(outcome.model.matrix), log=TRUE)
+ ll.y[df.temp[[response.var]]==0] <- plogis( outcome.params %*% t(outcome.model.matrix), log=TRUE,lower.tail=FALSE)
+ }
+
+ if(proxy_family$family=="binomial" & (proxy_family$link=='logit')){
+ proxy.model.matrix <- model.matrix(proxy_formula, df.temp)
+ ll.w <- vector(mode='numeric', length=dim(proxy.model.matrix)[1])
+ proxyvar <- with(df.temp,eval(parse(text=all.vars(proxy_formula)[1])))
+ ll.w[proxyvar==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==1,]),log=TRUE)
+ ll.w[proxyvar==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxyvar==0,]),log=TRUE,lower.tail=FALSE)
+ }
+
+ ## probability of the coded variables
+ coder.lls <- matrix(nrow=nrow(df.temp),ncol=length(coder_formulas))
+ ci <- 1
+ for(coder_formula in coder_formulas){
+ coder.model.matrix <- model.matrix(coder_formula, df.temp)
+ n.coder.model.covars <- dim(coder.model.matrix)[2]
+ coder.params <- params[param.idx:(n.coder.model.covars + param.idx - 1)]
+ param.idx <- param.idx + n.coder.model.covars
+ codervar <- with(df.temp, eval(parse(text=all.vars(coder_formula)[1])))
+ true.codervar <- df[[all.vars(coder_formula)[1]]]
+
+ ll.coder <- vector(mode='numeric', length=dim(coder.model.matrix)[1])
+ ll.coder[codervar==1] <- plogis(coder.params %*% t(coder.model.matrix[codervar==1,]),log=TRUE)
+ ll.coder[codervar==0] <- plogis(coder.params %*% t(coder.model.matrix[codervar==0,]),log=TRUE,lower.tail=FALSE)
+
+ # don't count when we know the observed value, unless we're accounting for observed value
+ ll.coder[(!is.na(true.codervar)) & (true.codervar != codervar)] <- NA
+ coder.lls[,ci] <- ll.coder
+ ci <- ci + 1
+ }
+
+ for(coder_formula in coder_formulas){
+ coder.model.matrix <- model.matrix(coder_formula, df.temp)
+ n.coder.model.covars <- dim(coder.model.matrix)[2]
+ param.idx <- param.idx - n.coder.model.covars
+ }
+
+ ll.parts[,i] <- ll.y + ll.w + apply(coder.lls,1,function(x) sum(x))
+
+ }
+
+ lls <- rowLogSumExps(ll.parts,na.rm=TRUE)
+
+ ## likelihood of observed data
+ target <- -1 * sum(lls)
+ print(target)
+ print(params)
+ return(target)
+ }
+ }
+
+ outcome.params <- colnames(model.matrix(outcome_formula,df))
+ response.var <- all.vars(outcome_formula)[1]
+ lower <- rep(-Inf, length(outcome.params))
+
+ if(outcome_family$family=='gaussian'){
+ params <- c(outcome.params, 'sigma_y')
+ lower <- c(lower, 0.00001)
+ } else {
+ params <- outcome.params
+ }
+
+ ## constrain the model of the coder and proxy vars
+ ## this is to ensure identifiability
+ ## it is a safe assumption because the coders aren't hostile (wrong more often than right)
+ ## so we can assume that y ~Bw, B is positive
+ proxy.params <- colnames(model.matrix(proxy_formula, df))
+ positive.params <- paste0('proxy_',response.var)
+ params <- c(params, paste0('proxy_',proxy.params))
+ lower <- c(lower, rep(-Inf, length(proxy.params)))
+ names(lower) <- params
+ lower[positive.params] <- 0.001
+
+ ci <- 0
+ for(coder_formula in coder_formulas){
+ coder.params <- colnames(model.matrix(coder_formula,df))
+ latent.coder.params <- coder.params %in% response.var
+ params <- c(params, paste0('coder_',ci,coder.params))
+ positive.params <- paste0('coder_',ci,response.var)
+ ci <- ci + 1
+ lower <- c(lower, rep(-Inf, length(coder.params)))
+ names(lower) <-params
+ lower[positive.params] <- 0.001
+ }
+
+ ## init by using the "loco model"
+ temp.df <- copy(df)
+ temp.df <- temp.df[y.obs.1 == y.obs.0, y:=y.obs.1]
+ loco.model <- glm(outcome_formula, temp.df, family=outcome_family)
+
+ start <- rep(1,length(params))
+ names(start) <- params
+ start[names(coef(loco.model))] <- coef(loco.model)
+ names(lower) <- params
+ if(method=='optim'){
+ print(lower)
+ fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE,control=list(maxit=1e6))
+ } else {
+
+ quoted.names <- gsub("[\\(\\)]",'',names(start))
+ print(quoted.names)
+ text <- paste("function(", paste0(quoted.names,'=',start,collapse=','),"){params<-c(",paste0(quoted.names,collapse=','),");return(nll(params))}")
+
+ measerr_mle_nll <- eval(parse(text=text))
+ names(start) <- quoted.names
+ names(lower) <- quoted.names
+ fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
+ }
+
+ return(fit)
+}
+
library(argparser)
parser <- arg_parser("Simulate data and fit corrected models.")
-parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
+parser <- add_argument(parser, "--infile", default="example_4.feather", help="name of the file to read.")
parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
args <- parse_args(parser)
change.remember.file(args$remember_file, clear=TRUE)
sims.df <- read_feather(args$infile)
sims.df[,Bzx:=NA]
+sims.df[,y_explained_variance:=NA]
sims.df[,accuracy_imbalance_difference:=NA]
plot.df <- build_plot_dataset(sims.df)
remember(median(sims.df$cor.xz),'med.cor.xz')
remember(median(sims.df$accuracy),'med.accuracy')
remember(median(sims.df$error.cor.x),'med.error.cor.x')
+remember(median(sims.df$error.cor.z),'med.error.cor.z')
remember(median(sims.df$lik.ratio),'med.lik.ratio')
parser <- arg_parser("Simulate data and fit corrected models.")
-parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
+parser <- add_argument(parser, "--infile", default="example_2.feather", help="name of the file to read.")
parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
args <- parse_args(parser)
z.amelia.full <- summarize.estimator(df, 'amelia.full', 'z')
- x.mecor <- summarize.estimator(df, 'mecor', 'x')
+ ## x.mecor <- summarize.estimator(df, 'mecor', 'x')
- z.mecor <- summarize.estimator(df, 'mecor', 'z')
+ ## z.mecor <- summarize.estimator(df, 'mecor', 'z')
- x.mecor <- summarize.estimator(df, 'mecor', 'x')
+ ## x.mecor <- summarize.estimator(df, 'mecor', 'x')
- z.mecor <- summarize.estimator(df, 'mecor', 'z')
+ ## z.mecor <- summarize.estimator(df, 'mecor', 'z')
x.mle <- summarize.estimator(df, 'mle', 'x')
z.gmm <- summarize.estimator(df, 'gmm', 'z')
accuracy <- df[,mean(accuracy)]
- plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.mecor, z.mecor, x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T)
+ plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T)
plot.df[,accuracy := accuracy]
plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
return(plot.df)
sims.df <- read_feather(args$infile)
+unique(sims.df[,.N,by=.(N,m)])
print(unique(sims.df$N))
# df <- df[apply(df,1,function(x) !any(is.na(x)))]
z.true <- summarize.estimator(df, 'true','z')
+ x.naive <- summarize.estimator(df, 'naive','x')
+
+ z.naive <- summarize.estimator(df, 'naive','z')
+
x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x')
z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z')
z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z')
+ z.loco.amelia <- summarize.estimator(df, 'amelia.full', 'z')
+ x.loco.amelia <- summarize.estimator(df, 'amelia.full', 'x')
+
+ z.loco.zhang <- summarize.estimator(df, 'zhang', 'z')
+ x.loco.zhang <- summarize.estimator(df, 'zhang', 'x')
+
accuracy <- df[,mean(accuracy)]
- plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, z.loco.mle, x.loco.mle),use.names=T)
+ plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.naive,z.naive,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, z.loco.mle, x.loco.mle, x.loco.amelia, z.loco.amelia, z.loco.zhang, x.loco.zhang),use.names=T)
plot.df[,accuracy := accuracy]
plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
return(plot.df)
z.true <- summarize.estimator(df, 'true','z')
+ x.naive <- summarize.estimator(df, 'naive','x')
+
+ z.naive <- summarize.estimator(df, 'naive','z')
+
x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x')
z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z')
z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z')
+ x.loco.mle <- summarize.estimator(df, 'loco.mle', 'x')
+
+ z.loco.amelia <- summarize.estimator(df, 'amelia.full', 'z')
+ x.loco.amelia <- summarize.estimator(df, 'amelia.full', 'x')
+
+ z.loco.zhang <- summarize.estimator(df, 'zhang', 'z')
+ x.loco.zhang <- summarize.estimator(df, 'zhang', 'x')
+
+
+ z.loco.gmm <- summarize.estimator(df, 'gmm', 'z')
+ x.loco.gmm <- summarize.estimator(df, 'gmm', 'x')
+
+
+
+
## x.mle <- summarize.estimator(df, 'mle', 'x')
## z.mle <- summarize.estimator(df, 'mle', 'z')
accuracy <- df[,mean(accuracy)]
- plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, x.loco.mle, z.loco.mle),use.names=T)
+ plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, x.loco.mle, z.loco.mle, x.loco.amelia, z.loco.amelia,x.loco.zhang, z.loco.zhang,x.loco.gmm, z.loco.gmm,x.naive,z.naive),use.names=T)
plot.df[,accuracy := accuracy]
plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
return(plot.df)
}
-plot.df <- read_feather(args$infile)
-print(unique(plot.df$N))
+sims.df <- read_feather(args$infile)
+print(unique(sims.df$N))
# df <- df[apply(df,1,function(x) !any(is.na(x)))]
-if(!('Bzx' %in% names(plot.df)))
- plot.df[,Bzx:=NA]
+if(!('Bzx' %in% names(sims.df)))
+ sims.df[,Bzx:=NA]
-if(!('accuracy_imbalance_difference' %in% names(plot.df)))
- plot.df[,accuracy_imbalance_difference:=NA]
+if(!('accuracy_imbalance_difference' %in% names(sims.df)))
+ sims.df[,accuracy_imbalance_difference:=NA]
-unique(plot.df[,'accuracy_imbalance_difference'])
+unique(sims.df[,'accuracy_imbalance_difference'])
#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
-plot.df <- build_plot_dataset(plot.df)
+plot.df <- build_plot_dataset(sims.df)
change.remember.file("remember_irr.RDS",clear=TRUE)
remember(plot.df,args$name)
+
+set.remember.prefix(gsub("plot.df.","",args$name))
+remember(median(sims.df$loco.accuracy),'med.loco.acc')
+
#ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
## ## ## df[gmm.ER_pval<0.05]
#!/bin/bash
#SBATCH --job-name="simulate measurement error models"
## Allocation Definition
-#SBATCH --account=comdata
-#SBATCH --partition=compute-bigmem
+#SBATCH --account=comdata-ckpt
+#SBATCH --partition=ckpt
## Resources
#SBATCH --nodes=1
## Walltime (4 hours)
TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
TASK_CALL=$(sed -n ${TASK_NUM}p $2)
+echo ${TASK_CALL}
${TASK_CALL}
library(bbmle)
library(matrixStats) # for numerically stable logsumexps
+source("pl_methods.R")
source("measerr_methods.R") ## for my more generic function.
## This uses the pseudolikelihood approach from Carroll page 349.
}
-
-## model from Zhang's arxiv paper, with predictions for y
-## Zhang got this model from Hausman 1998
-### I think this is actually eqivalent to the pseudo.mle method
-zhang.mle.iv <- function(df){
- df.obs <- df[!is.na(x.obs)]
- df.unobs <- df[is.na(x.obs)]
-
- tn <- df.obs[(w_pred == 0) & (x.obs == w_pred),.N]
- pn <- df.obs[(w_pred==0), .N]
- npv <- tn / pn
-
- tp <- df.obs[(w_pred==1) & (x.obs == w_pred),.N]
- pp <- df.obs[(w_pred==1),.N]
- ppv <- tp / pp
-
- nll <- function(B0=0, Bxy=0, Bzy=0, sigma_y=0.1){
-
- ## fpr = 1 - TNR
- ### Problem: accounting for uncertainty in ppv / npv
-
- ## fnr = 1 - TPR
- ll.y.obs <- with(df.obs, dnorm(y, B0 + Bxy * x + Bzy * z, sd=sigma_y,log=T))
- ll <- sum(ll.y.obs)
-
- # unobserved case; integrate out x
- ll.x.1 <- with(df.unobs, dnorm(y, B0 + Bxy + Bzy * z, sd = sigma_y, log=T))
- ll.x.0 <- with(df.unobs, dnorm(y, B0 + Bzy * z, sd = sigma_y,log=T))
-
- ## case x == 1
- lls.x.1 <- colLogSumExps(rbind(log(ppv) + ll.x.1, log(1-ppv) + ll.x.0))
-
- ## case x == 0
- lls.x.0 <- colLogSumExps(rbind(log(1-npv) + ll.x.1, log(npv) + ll.x.0))
-
- lls <- colLogSumExps(rbind(df.unobs$w_pred * lls.x.1, (1-df.unobs$w_pred) * lls.x.0))
- ll <- ll + sum(lls)
- return(-ll)
- }
- mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6), lower=list(sigma_y=0.0001, B0=-Inf, Bxy=-Inf, Bzy=-Inf),
- upper=list(sigma_y=Inf, B0=Inf, Bxy=Inf, Bzy=Inf),method='L-BFGS-B')
- return(mlefit)
-}
-
-## this is equivalent to the pseudo-liklihood model from Caroll
-## zhang.mle.dv <- function(df){
-
-## nll <- function(B0=0, Bxy=0, Bzy=0, ppv=0.9, npv=0.9){
-## df.obs <- df[!is.na(y.obs)]
-
-## ## fpr = 1 - TNR
-## ll.w0y0 <- with(df.obs[y.obs==0],dbinom(1-w_pred,1,npv,log=TRUE))
-## ll.w1y1 <- with(df.obs[y.obs==1],dbinom(w_pred,1,ppv,log=TRUE))
-
-## # observed case
-## ll.y.obs <- vector(mode='numeric', length=nrow(df.obs))
-## ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T))
-## ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE))
-
-## ll <- sum(ll.y.obs) + sum(ll.w0y0) + sum(ll.w1y1)
-
-## # unobserved case; integrate out y
-## ## case y = 1
-## ll.y.1 <- vector(mode='numeric', length=nrow(df))
-## pi.y.1 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T))
-## ## P(w=1| y=1)P(y=1) + P(w=0|y=1)P(y=1) = P(w=1,y=1) + P(w=0,y=1)
-## lls.y.1 <- colLogSumExps(rbind(log(ppv) + pi.y.1, log(1-ppv) + pi.y.1))
-
-## ## case y = 0
-## ll.y.0 <- vector(mode='numeric', length=nrow(df))
-## pi.y.0 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE))
-
-## ## P(w=1 | y=0)P(y=0) + P(w=0|y=0)P(y=0) = P(w=1,y=0) + P(w=0,y=0)
-## lls.y.0 <- colLogSumExps(rbind(log(npv) + pi.y.0, log(1-npv) + pi.y.0))
-
-## lls <- colLogSumExps(rbind(lls.y.1, lls.y.0))
-## ll <- ll + sum(lls)
-## return(-ll)
-## }
-## mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=list(B0=-Inf, Bxy=-Inf, Bzy=-Inf, ppv=0.001,npv=0.001),
-## upper=list(B0=Inf, Bxy=Inf, Bzy=Inf,ppv=0.999,npv=0.999))
-## return(mlefit)
-## }
-
-zhang.mle.dv <- function(df){
- df.obs <- df[!is.na(y.obs)]
- df.unobs <- df[is.na(y.obs)]
-
- fp <- df.obs[(w_pred==1) & (y.obs != w_pred),.N]
- p <- df.obs[(w_pred==1),.N]
- fpr <- fp / p
- fn <- df.obs[(w_pred==0) & (y.obs != w_pred), .N]
- n <- df.obs[(w_pred==0),.N]
- fnr <- fn / n
-
- nll <- function(B0=0, Bxy=0, Bzy=0){
-
-
- ## observed case
- ll.y.obs <- vector(mode='numeric', length=nrow(df.obs))
- ll.y.obs[df.obs$y.obs==1] <- with(df.obs[y.obs==1], plogis(B0 + Bxy * x + Bzy * z,log=T))
- ll.y.obs[df.obs$y.obs==0] <- with(df.obs[y.obs==0], plogis(B0 + Bxy * x + Bzy * z,log=T,lower.tail=FALSE))
-
- ll <- sum(ll.y.obs)
-
- pi.y.1 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T))
- pi.y.0 <- with(df,plogis(B0 + Bxy * x + Bzy*z, log=T,lower.tail=FALSE))
-
- lls <- with(df.unobs, colLogSumExps(rbind(w_pred * colLogSumExps(rbind(log(fpr), log(1 - fnr - fpr)+pi.y.1)),
- (1-w_pred) * colLogSumExps(rbind(log(1-fpr), log(1 - fnr - fpr)+pi.y.0)))))
-
- ll <- ll + sum(lls)
- return(-ll)
- }
- mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=c(B0=-Inf, Bxy=-Inf, Bzy=-Inf),
- upper=c(B0=Inf, Bxy=Inf, Bzy=Inf))
- return(mlefit)
-}
## This uses the likelihood approach from Carroll page 353.
## assumes that we have a good measurement error model
run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~y){
- accuracy <- df[,mean(w_pred==y)]
+ (accuracy <- df[,mean(w_pred==y)])
result <- append(result, list(accuracy=accuracy))
- error.cor.x <- cor(df$x, df$w - df$x)
- result <- append(result, list(error.cor.x = error.cor.x))
+ (error.cor.z <- cor(df$z, df$y - df$w_pred))
+ (error.cor.x <- cor(df$x, df$y - df$w_pred))
+ (error.cor.y <- cor(df$y, df$y - df$w_pred))
+ result <- append(result, list(error.cor.x = error.cor.x,
+ error.cor.z = error.cor.z,
+ error.cor.y = error.cor.y))
model.null <- glm(y~1, data=df,family=binomial(link='logit'))
(model.true <- glm(y ~ x + z, data=df,family=binomial(link='logit')))
true.ci.Bxy <- confint(model.true)['x',]
true.ci.Bzy <- confint(model.true)['z',]
-
+ result <- append(result, list(cor.xz=cor(df$x,df$z)))
result <- append(result, list(lik.ratio=lik.ratio))
result <- append(result, list(Bxy.est.true=coef(model.true)['x'],
# amelia says use normal distribution for binary variables.
- tryCatch({
- amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
- mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
- (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
- est.x.mi <- coefse['x','Estimate']
- est.x.se <- coefse['x','Std.Error']
- result <- append(result,
- list(Bxy.est.amelia.full = est.x.mi,
- Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
- Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se
- ))
-
- est.z.mi <- coefse['z','Estimate']
- est.z.se <- coefse['z','Std.Error']
- result <- append(result,
- list(Bzy.est.amelia.full = est.z.mi,
- Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se,
- Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
- ))
+ amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
+ mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
+ (coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
+ est.x.mi <- coefse['x','Estimate']
+ est.x.se <- coefse['x','Std.Error']
+ result <- append(result,
+ list(Bxy.est.amelia.full = est.x.mi,
+ Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
+ Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se
+ ))
- },
- error = function(e){
- message("An error occurred:\n",e)
- result$error <- paste0(result$error,'\n', e)
- })
+ est.z.mi <- coefse['z','Estimate']
+ est.z.se <- coefse['z','Std.Error']
+ result <- append(result,
+ list(Bzy.est.amelia.full = est.z.mi,
+ Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se,
+ Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
+ ))
return(result)
Bzy.ci.lower.naive = naive.ci.Bzy[1]))
- tryCatch({
+
amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('x','w'))
mod.amelia.k <- zelig(y~x.obs+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
(coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
))
- },
- error = function(e){
- message("An error occurred:\n",e)
- result$error <-paste0(result$error,'\n', e)
- }
- )
- tryCatch({
temp.df <- copy(df)
temp.df <- temp.df[,x:=x.obs]
mod.caroll.lik <- measerr_mle(temp.df, outcome_formula=outcome_formula, proxy_formula=proxy_formula, truth_formula=truth_formula)
Bzy.est.mle = coef['z'],
Bzy.ci.upper.mle = ci.upper['z'],
Bzy.ci.lower.mle = ci.lower['z']))
- },
-
- error = function(e){
- message("An error occurred:\n",e)
- result$error <- paste0(result$error,'\n', e)
- })
-
- tryCatch({
mod.zhang.lik <- zhang.mle.iv(df)
coef <- coef(mod.zhang.lik)
Bzy.est.zhang = coef['Bzy'],
Bzy.ci.upper.zhang = ci['Bzy','97.5 %'],
Bzy.ci.lower.zhang = ci['Bzy','2.5 %']))
- },
-
- error = function(e){
- message("An error occurred:\n",e)
- result$error <- paste0(result$error,'\n', e)
- })
## What if we can't observe k -- most realistic scenario. We can't include all the ML features in a model.
## amelia.out.nok <- amelia(df, m=200, p2s=0, idvars=c("x","w_pred"), noms=noms)
Bzy.ci.lower.gmm = gmm.res$confint[2,1]))
- tryCatch({
- mod.calibrated.mle <- mecor(y ~ MeasError(w_pred, reference = x.obs) + z, df, B=400, method='efficient')
- (mod.calibrated.mle)
- (mecor.ci <- summary(mod.calibrated.mle)$c$ci['x.obs',])
- result <- append(result, list(
- Bxy.est.mecor = mecor.ci['Estimate'],
- Bxy.ci.upper.mecor = mecor.ci['UCI'],
- Bxy.ci.lower.mecor = mecor.ci['LCI'])
- )
-
- (mecor.ci <- summary(mod.calibrated.mle)$c$ci['z',])
-
- result <- append(result, list(
- Bzy.est.mecor = mecor.ci['Estimate'],
- Bzy.ci.upper.mecor = mecor.ci['UCI'],
- Bzy.ci.lower.mecor = mecor.ci['LCI'])
- )
- },
- error = function(e){
- message("An error occurred:\n",e)
- result$error <- paste0(result$error, '\n', e)
- }
- )
+ ## tryCatch({
+ ## mod.calibrated.mle <- mecor(y ~ MeasError(w_pred, reference = x.obs) + z, df, B=400, method='efficient')
+ ## (mod.calibrated.mle)
+ ## (mecor.ci <- summary(mod.calibrated.mle)$c$ci['x.obs',])
+ ## result <- append(result, list(
+ ## Bxy.est.mecor = mecor.ci['Estimate'],
+ ## Bxy.ci.upper.mecor = mecor.ci['UCI'],
+ ## Bxy.ci.lower.mecor = mecor.ci['LCI'])
+ ## )
+
+ ## (mecor.ci <- summary(mod.calibrated.mle)$c$ci['z',])
+
+ ## result <- append(result, list(
+ ## Bzy.est.mecor = mecor.ci['Estimate'],
+ ## Bzy.ci.upper.mecor = mecor.ci['UCI'],
+ ## Bzy.ci.lower.mecor = mecor.ci['LCI'])
+ ## )
+ ## },
+ ## error = function(e){
+ ## message("An error occurred:\n",e)
+ ## result$error <- paste0(result$error, '\n', e)
+ ## }
+ ## )
## clean up memory
## rm(list=c("df","y","x","g","w","v","train","p","amelia.out.k","amelia.out.nok", "mod.calibrated.mle","gmm.res","mod.amelia.k","mod.amelia.nok", "model.true","model.naive","model.feasible"))
var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.975,na.rm=T),
est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.025,na.rm=T),
- mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]]),
- mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]]),
+ mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],na.rm=T),
+ mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],na.rm=T),
ci.upper.975 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.975,na.rm=T),
ci.upper.025 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.025,na.rm=T),
ci.lower.975 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.975,na.rm=T),
projects / ml_measurement_error_public.git / commitdiff